Clonal evolution: CLL to Richter transformation
2021/04/09
1 Introduction
The purpose of this notebook is to visualize the clonal transcriptomic evolution in Richter syndrome.
2 Pre-processing
2.1 Load packages
library(Seurat)
library(SeuratWrappers)
library(harmony)
library(tidyverse)2.2 Define parameters
# Paths
path_to_12 <- here::here("results/R_objects/6.seurat_annotated_12.rds")
path_to_19 <- here::here("results/R_objects/6.seurat_annotated_19.rds")
path_to_63 <- here::here("results/R_objects/patient_63/3.seurat_annotated.rds")
path_to_365 <- here::here("results/R_objects/6.seurat_annotated_365.rds")
path_to_3299 <- here::here("results/R_objects/6.seurat_annotated_3299.rds")
# Colors
color_palette <- c("black", "gray", "red", "yellow", "violet", "green4",
"blue", "mediumorchid2", "coral2", "blueviolet",
"indianred4", "deepskyblue1", "dimgray", "deeppink1",
"green", "lightgray", "hotpink1")
# Source functions
source(here::here("bin/utils.R"))2.3 Load data
paths_to_load <- c(
"12" = path_to_12,
"19" = path_to_19,
"63" = path_to_63,
"365" = path_to_365,
"3299" = path_to_3299
)
seurat_list <- purrr::map(paths_to_load, readRDS)
seurat_list## $`12`
## An object of class Seurat
## 23326 features across 5785 samples within 1 assay
## Active assay: RNA (23326 features, 2000 variable features)
## 3 dimensional reductions calculated: pca, harmony, umap
##
## $`19`
## An object of class Seurat
## 23326 features across 7284 samples within 1 assay
## Active assay: RNA (23326 features, 2000 variable features)
## 3 dimensional reductions calculated: pca, harmony, umap
##
## $`63`
## An object of class Seurat
## 13680 features across 983 samples within 1 assay
## Active assay: RNA (13680 features, 2000 variable features)
## 2 dimensional reductions calculated: pca, umap
##
## $`365`
## An object of class Seurat
## 23326 features across 4685 samples within 1 assay
## Active assay: RNA (23326 features, 2000 variable features)
## 3 dimensional reductions calculated: pca, harmony, umap
##
## $`3299`
## An object of class Seurat
## 23326 features across 6063 samples within 1 assay
## Active assay: RNA (23326 features, 2000 variable features)
## 3 dimensional reductions calculated: pca, harmony, umappurrr::map(seurat_list, DimPlot, cols = color_palette)## $`12`
##
## $`19`
##
## $`63`
##
## $`365`
##
## $`3299`
3 Clonal evolution
3.1 UMAPs splitted by time point
seurat_list <- purrr::map(seurat_list, function(seurat_obj) {
seurat_obj@meta.data <- seurat_obj@meta.data %>%
mutate(sample_description_RM = str_c(
time_point,
sample_description_FN,
sep = "_"
))
seurat_obj
})
umaps_time_points <- purrr::map(names(seurat_list), function(x) {
p <- plot_split_umap(seurat_list[[x]], "sample_description_RM", pt_size = 0.5) +
ggtitle(x) +
theme(plot.title = element_text(size = 13, hjust = 0.5, face = "bold"))
p
})
names(umaps_time_points) <- names(seurat_list)
umaps_time_points## $`12`
##
## $`19`
##
## $`63`
##
## $`365`
##
## $`3299`
3.2 Stacked bar plots
Let us start by computing the proportions of clons across time points:
proportions_dfs <- purrr::map(seurat_list, function(seurat_obj) {
df <- seurat_obj@meta.data %>%
select("sample_description_RM", "annotation_final") %>%
group_by(sample_description_RM, annotation_final) %>%
summarise(n_cells = n()) %>%
ungroup() %>%
group_by(sample_description_RM) %>%
mutate(n_cells_total = sum(n_cells)) %>%
ungroup() %>%
mutate(percentage_cells = round(n_cells / n_cells_total * 100, 3))
df
})
DT::datatable(proportions_dfs$`12`)DT::datatable(proportions_dfs$`19`)DT::datatable(proportions_dfs$`63`)DT::datatable(proportions_dfs$`365`)DT::datatable(proportions_dfs$`3299`)Plots:
stacked_bars_ggs <- purrr::map(names(proportions_dfs), function(x) {
p <- proportions_dfs[[x]] %>%
ggplot(aes(sample_description_RM, percentage_cells, fill = annotation_final)) +
geom_col() +
ggtitle(x) +
labs(x = "", y = "Percentage of Cells (%)", fill = "") +
scale_fill_manual(values = color_palette) +
theme_bw() +
theme(
plot.title = element_text(size = 13, hjust = 0.5, face = "bold"),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)
)
p
})
names(stacked_bars_ggs) <- names(seurat_list)
stacked_bars_ggs## $`12`
##
## $`19`
##
## $`63`
##
## $`365`
##
## $`3299`
4 Session Inforamation
sessionInfo()## R version 4.0.4 (2021-02-15)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.2 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=es_ES.UTF-8 LC_COLLATE=en_US.UTF-8 LC_MONETARY=es_ES.UTF-8 LC_MESSAGES=en_US.UTF-8 LC_PAPER=es_ES.UTF-8 LC_NAME=C LC_ADDRESS=C LC_TELEPHONE=C LC_MEASUREMENT=es_ES.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] forcats_0.5.1 stringr_1.4.0 dplyr_1.0.6 purrr_0.3.4 readr_1.4.0 tidyr_1.1.3 tibble_3.1.2 ggplot2_3.3.3 tidyverse_1.3.1 harmony_1.0 Rcpp_1.0.6 SeuratWrappers_0.3.0 SeuratObject_4.0.2 Seurat_4.0.3 BiocStyle_2.18.1
##
## loaded via a namespace (and not attached):
## [1] readxl_1.3.1 backports_1.2.1 plyr_1.8.6 igraph_1.2.6 lazyeval_0.2.2 splines_4.0.4 crosstalk_1.1.1 listenv_0.8.0 scattermore_0.7 digest_0.6.27 htmltools_0.5.1.1 fansi_0.5.0 magrittr_2.0.1 tensor_1.5 cluster_2.1.1 ROCR_1.0-11 remotes_2.4.0 globals_0.14.0 modelr_0.1.8 matrixStats_0.59.0 spatstat.sparse_2.0-0 colorspace_2.0-1 rvest_1.0.0 ggrepel_0.9.1 haven_2.4.1 xfun_0.23 crayon_1.4.1 jsonlite_1.7.2 spatstat.data_2.1-0 survival_3.2-10 zoo_1.8-9 glue_1.4.2 polyclip_1.10-0 gtable_0.3.0 leiden_0.3.8 future.apply_1.7.0 abind_1.4-5 scales_1.1.1 DBI_1.1.1 miniUI_0.1.1.1 viridisLite_0.4.0 xtable_1.8-4 reticulate_1.20 spatstat.core_2.1-2 rsvd_1.0.5 DT_0.18 htmlwidgets_1.5.3 httr_1.4.2 RColorBrewer_1.1-2 ellipsis_0.3.2 ica_1.0-2 farver_2.1.0 pkgconfig_2.0.3 sass_0.4.0
## [55] uwot_0.1.10 dbplyr_2.1.1 deldir_0.2-10 here_1.0.1 utf8_1.2.1 labeling_0.4.2 tidyselect_1.1.1 rlang_0.4.11 reshape2_1.4.4 later_1.2.0 munsell_0.5.0 cellranger_1.1.0 tools_4.0.4 cli_2.5.0 generics_0.1.0 broom_0.7.7 ggridges_0.5.3 evaluate_0.14 fastmap_1.1.0 yaml_2.2.1 goftest_1.2-2 knitr_1.33 fs_1.5.0 fitdistrplus_1.1-5 RANN_2.6.1 pbapply_1.4-3 future_1.21.0 nlme_3.1-152 mime_0.10 xml2_1.3.2 compiler_4.0.4 rstudioapi_0.13 plotly_4.9.4 png_0.1-7 spatstat.utils_2.2-0 reprex_2.0.0 bslib_0.2.5.1 stringi_1.6.2 highr_0.9 lattice_0.20-41 Matrix_1.3-4 vctrs_0.3.8 pillar_1.6.1 lifecycle_1.0.0 BiocManager_1.30.15 spatstat.geom_2.1-0 lmtest_0.9-38 jquerylib_0.1.4 RcppAnnoy_0.0.18 data.table_1.14.0 cowplot_1.1.1 irlba_2.3.3 httpuv_1.6.1 patchwork_1.1.1
## [109] R6_2.5.0 bookdown_0.22 promises_1.2.0.1 KernSmooth_2.23-18 gridExtra_2.3 parallelly_1.26.0 codetools_0.2-18 MASS_7.3-53.1 assertthat_0.2.1 rprojroot_2.0.2 withr_2.4.2 sctransform_0.3.2 mgcv_1.8-36 parallel_4.0.4 hms_1.1.0 grid_4.0.4 rpart_4.1-15 rmarkdown_2.8 Rtsne_0.15 shiny_1.6.0 lubridate_1.7.10